class_docstr: Image Cascade Network (ICNet)
  
  Parameters
  ----------
  nclass : int
      Number of categories for the training dataset.
  backbone : string
      Pre-trained dilated backbone network type (default:'resnet50'; 'resnet50',
      'resnet101' or 'resnet152').
  norm_layer : object
      Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;
      for Synchronized Cross-GPU BachNormalization).
  norm_kwargs : dict
      Additional `norm_layer` arguments, for example `num_devices=4`
      for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
  pretrained_base : bool or str
      Refers to if the backbone is pretrained or not. If `True`,
      model weights of a model that was trained on ImageNet is loaded.
  
  Reference:
  
      Hengshuang Zhao, Xiaojuan Qi, Xiaoyong Shen, Jianping Shi and Jiaya Jia.
      "ICNet for Real-Time Semantic Segmentation on High-Resolution Images." *ECCV*, 2018
  
  Examples
  --------
  >>> model = ICNet(nclass=19, backbone='resnet50')
  >>> print(model)
code: def demo(self, x):
      return self.predict(x)
  
imports: from __future__ import division
  from mxnet.gluon import nn
  from mxnet.context import cpu
  from mxnet.gluon.nn import HybridBlock
  from gluoncv.model_zoo.segbase import SegBaseModel
  from gluoncv.model_zoo.pspnet import _PSPHead
label_desc: Use image node_id 3 for calling the ICNet obj's underlying member method code with example usage: obj.demo(x) and returns: self